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Zhang Q, Yang M, Zhang P, Wu B, Wei X, Li S. Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0129. [PMID: 37589244 PMCID: PMC11033716 DOI: 10.20892/j.issn.2095-3941.2023.0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/26/2023] [Indexed: 08/18/2023] Open
Abstract
Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.
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Affiliation(s)
- Qian Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Mingran Yang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Bowen Wu
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaosen Wei
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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Chen Q, Li Y, Tan K, Qiao Y, Pan S, Jiang T, Chen YPP. Network-based methods for gene function prediction. Brief Funct Genomics 2021; 20:249-257. [PMID: 33686431 DOI: 10.1093/bfgp/elab006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 12/23/2022] Open
Abstract
The rapid development of high-throughput technology has generated a large number of biological networks. Network-based methods are able to provide rich information for inferring gene function. This is composed of analyzing the topological characteristics of genes in related networks, integrating biological information, and considering data from different data sources. To promote network biology and related biotechnology research, this article provides a survey for the state of the art of advanced methods of network-based gene function prediction and discusses the potential challenges.
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Affiliation(s)
- Qingfeng Chen
- University of Technology Sydney, China and Hundred-Talent Program
| | - Yongjie Li
- School of Computer and Electronic Information at Guangxi University
| | - Kai Tan
- School of Computer and Electronic Information at Guangxi University
| | - Yvlu Qiao
- School of Computer and Electronic Information at Guangxi University
| | - Shirui Pan
- Computer science from the University of Technology Sydney
| | - Taijiao Jiang
- Suzhou Institute of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia
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Wei T, Fa B, Luo C, Johnston L, Zhang Y, Yu Z. An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery. Front Genet 2021; 11:613033. [PMID: 33488678 PMCID: PMC7820902 DOI: 10.3389/fgene.2020.613033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 11/25/2020] [Indexed: 12/25/2022] Open
Abstract
Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.
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Affiliation(s)
- Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Chengwen Luo
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Luke Johnston
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
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Gao D, Chen Q, Zeng Y, Jiang M, Zhang Y. Applications of Machine Learning in Drug Target Discovery. Curr Drug Metab 2020; 21:790-803. [PMID: 32723266 DOI: 10.2174/1567201817999200728142023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/12/2020] [Accepted: 05/13/2020] [Indexed: 12/15/2022]
Abstract
Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.
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Affiliation(s)
- Dongrui Gao
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Qingyuan Chen
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuanqi Zeng
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Meng Jiang
- School of Mechanical Automotive Engineering, Nanyang Institute of Technology, Nanyang 473000, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
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Ata SK, Wu M, Fang Y, Ou-Yang L, Kwoh CK, Li XL. Recent advances in network-based methods for disease gene prediction. Brief Bioinform 2020; 22:6023077. [PMID: 33276376 DOI: 10.1093/bib/bbaa303] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/29/2020] [Accepted: 10/10/2020] [Indexed: 01/28/2023] Open
Abstract
Disease-gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative and complementary low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. In this survey, we aim to provide a comprehensive and up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.
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Affiliation(s)
- Sezin Kircali Ata
- School of Computer Science and Engineering Nanyang Technological University (NTU)
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, Singapore
| | - Yuan Fang
- School of Information Systems, Singapore Management University, Singapore
| | - Le Ou-Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen China
| | | | - Xiao-Li Li
- Department head and principal scientist at I2R, A*STAR, Singapore
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Zhou L, Li Z, Yang J, Tian G, Liu F, Wen H, Peng L, Chen M, Xiang J, Peng L. Revealing Drug-Target Interactions with Computational Models and Algorithms. Molecules 2019; 24:molecules24091714. [PMID: 31052598 PMCID: PMC6540161 DOI: 10.3390/molecules24091714] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 04/24/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022] Open
Abstract
Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | | | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing 100102, China.
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Hong Wen
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Li Peng
- School of Computer Science, University of Science and Technology of Hunan, Xiangtan 411201, Hunan, China.
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
- Neuroscience Research Center, Department of Basic Medical Sciences, Changsha Medical University, Changsha 410219, Hunan, China.
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
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Ding D, Sun X. Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1. Genes (Basel) 2018; 9:E41. [PMID: 29337910 PMCID: PMC5793192 DOI: 10.3390/genes9010041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/07/2018] [Accepted: 01/12/2018] [Indexed: 12/22/2022] Open
Abstract
Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c-type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.
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Affiliation(s)
- Dewu Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
- Department of Mathematics and Computer Science, Chizhou College, Chizhou 247000, China.
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
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